SoroushVahidi/scidocs huggingface dataset:
Social interaction based video recommendation: Recommending YouTube videos to facebook users
(2014)

Online videos, e.g., YouTube videos, are important topics for social interactions among users of online social networking sites (OSN), e.g., Facebook. This opens up the possibility of exploiting video-related user social interaction information for better video recommendation. Towards this goal, we conduct a case study of recommending YouTube videos to Facebook users based on their social interactions. We first measure social interactions related to YouTube videos among Facebook users. We observe that the attention a video attracts on Facebook is not always well-aligned with its popularity on YouTube. Unpopular videos on YouTube can become popular on Facebook, while popular videos on YouTube often do not attract proportionally high attentions on Facebook. This finding motivates us to develop a simple top-k video recommendation algorithm that exploits user social interaction information to improve the recommendation accuracy for niche videos, that are globally unpopular, but highly relevant to a specific user or user group. Through experiments on the collected Facebook traces, we demonstrate that our recommendation algorithm significantly outperforms the YouTube-popularity based video recommendation algorithm as well as a collaborative filtering algorithm based on user similarities.
TextLuas: Tracking and Visualizing Document and Term Clusters in Dynamic Text Data
(2015)

For large volumes of text data collected over time, a key knowledge discovery task is identifying and tracking clusters. These clusters may correspond to emerging themes, popular topics, or breaking news stories in a corpus. Therefore, recently there has been increased interest in the problem of clustering dynamic data. However, there exists little support for the interactive exploration of the output of these analysis techniques, particularly in cases where researchers wish to simultaneously explore both the change in cluster structure over time and the change in the textual content associated with clusters. In this paper, we propose a model for tracking dynamic clusters characterized by the evolutionary events of each cluster. Motivated by this model, the TextLuas system provides an implementation for tracking these dynamic clusters and visualizing their evolution using a metro map metaphor. To provide overviews of cluster content, we adapt the tag cloud representation to the dynamic clustering scenario. We demonstrate the TextLuas system on two different text corpora, where they are shown to elucidate the evolution of key themes. We also describe how TextLuas was applied to a problem in bibliographic network research.
Signal processing of range detection for SFCW radars using Matlab and GNU radio
(2014)

Development of radar technology is now rapidly. One of them is Step Frequency Continuous Wave Radar (SFCW Radar). SFCW radar can be used for various purposes. SFCW radar consists of antenna, control unit, signal processing unit. The radar advantages compared to the other radar are this radar is easier to implement and more widely within the range of radar. In this research that will be done is design SFCW radar. There are several stages to be performed in this research. At first, the simulation will be done using Matlab®. This simulation will determine the parameters needed to obtain an appropriate resolution. The second, simulations is performed by using GNU radio. This simulation will be adjusted to the parameters that have been previously designed by Matlab®. Both of the results will be analyzed. Input signal is complex. Simulation results for 5kHz-1.285MHz will display in graph. Graph will show location of the object in 5 m, 1km, and 1.1km depend on initial design.
Weather classification with deep convolutional neural networks
(2015)

In this paper, we study weather classification from images using Convolutional Neural Networks (CNNs). Our approach outperforms the state of the art by a huge margin in the weather classification task. Our approach achieves 82.2% normalized classification accuracy instead of 53.1% for the state of the art (i.e., 54.8% relative improvement). We also studied the behavior of all the layers of the Convolutional Neural Networks, we adopted, and interesting findings are discussed.
Image Deblurring via Enhanced Low-Rank Prior
(2016)

Low-rank matrix approximation has been successfully applied to numerous vision problems in recent years. In this paper, we propose a novel low-rank prior for blind image deblurring. Our key observation is that directly applying a simple low-rank model to a blurry input image significantly reduces the blur even without using any kernel information, while preserving important edge information. The same model can be used to reduce blur in the gradient map of a blurry input. Based on these properties, we introduce an enhanced prior for image deblurring by combining the low rank prior of similar patches from both the blurry image and its gradient map. We employ a weighted nuclear norm minimization method to further enhance the effectiveness of low-rank prior for image deblurring, by retaining the dominant edges and eliminating fine texture and slight edges in intermediate images, allowing for better kernel estimation. In addition, we evaluate the proposed enhanced low-rank prior for both the uniform and the non-uniform deblurring. Quantitative and qualitative experimental evaluations demonstrate that the proposed algorithm performs favorably against the state-of-the-art deblurring methods.
Functional connectivity dynamically evolves on multiple time-scales over a static structural connectome: Models and mechanisms
(2017)

Over the last decade, we have observed a revolution in brain structural and functional Connectomics. On one hand, we have an ever-more detailed characterization of the brain's white matter structural connectome. On the other, we have a repertoire of consistent functional networks that form and dissipate over time during rest. Despite the evident spatial similarities between structural and functional connectivity, understanding how different time-evolving functional networks spontaneously emerge from a single structural network requires analyzing the problem from the perspective of complex network dynamics and dynamical system's theory. In that direction, bottom-up computational models are useful tools to test theoretical scenarios and depict the mechanisms at the genesis of resting-state activity. Here, we provide an overview of the different mechanistic scenarios proposed over the last decade via computational models. Importantly, we highlight the need of incorporating additional model constraints considering the properties observed at finer temporal scales with MEG and the dynamical properties of FC in order to refresh the list of candidate scenarios.
Spider Monkey Optimization algorithm for numerical optimization
(2014)

Swarm intelligence is one of the most promising area for the researchers in the field of numerical optimization. Researchers have developed many algorithms by simulating the swarming behavior of various creatures like ants, honey bees, fish, birds and the findings are very motivating. In this paper, a new approach for numerical optimization is proposed by modeling the foraging behavior of spider monkeys. Spider monkeys have been categorized as fission–fusion social structure based animals. The animals which follow fission– fusion social systems, split themselves from large to smaller groups and vice-versa based on the scarcity or availability of food. The proposed swarm intelligence approach is named as Spider Monkey Optimization (SMO) algorithm and can broadly be classified as an algorithm inspired by intelligent foraging behavior of fission–fusion social structure based animals.
Modelling Domain Relationships for Transfer Learning on Retrieval-based Question Answering Systems in E-commerce
(2018)

Nowadays, it is a heated topic for many industries to build automatic question-answering (QA) systems. A key solution to these QA systems is to retrieve from a QA knowledge base the most similar question of a given question, which can be reformulated as a paraphrase identification (PI) or a natural language inference (NLI) problem. However, most existing models for PI and NLI have at least two problems: They rely on a large amount of labeled data, which is not always available in real scenarios, and they may not be efficient for industrial applications. In this paper, we study transfer learning for the PI and NLI problems, aiming to propose a general framework, which can effectively and efficiently adapt the shared knowledge learned from a resource-rich source domain to a resource-poor target domain. Specifically, since most existing transfer learning methods only focus on learning a shared feature space across domains while ignoring the relationship between the source and target domains, we propose to simultaneously learn shared representations and domain relationships in a unified framework. Furthermore, we propose an efficient and effective hybrid model by combining a sentence encoding-based method and a sentence interaction-based method as our base model. Extensive experiments on both paraphrase identification and natural language inference demonstrate that our base model is efficient and has promising performance compared to the competing models, and our transfer learning method can help to significantly boost the performance. Further analysis shows that the inter-domain and intra-domain relationship captured by our model are insightful. Last but not least, we deploy our transfer learning model for PI into our online chatbot system, which can bring in significant improvements over our existing system. Finally, we launch our new system on the chatbot platform Eva in our E-commerce site AliExpress.
Generation of circularly polarized conical beam pattern using toroidal helical knot antenna
(2016)

A novel circularly polarized antenna with a conical beam radiation pattern is presented. It consists of a feeding probe and a (1, 6) helical torus knot as a polarizer. Measured results shows that, the antenna has an impedance bandwidth of 19.67% and axial ratio bandwidth of 15.7%. With the RHCP gain of about 6 dBic at 3.5 GHz. Toroidal helical knot antenna is mechanically simple to fabricate using additive manufacturing technology. The proposed prototype is suitable for mounting on vehicles to facilitate communication with geostationary satellites.
Towards deep learning with segregated dendrites
(2017)

Deep learning has led to significant advances in artificial intelligence, in part, by adopting strategies motivated by neurophysiology. However, it is unclear whether deep learning could occur in the real brain. Here, we show that a deep learning algorithm that utilizes multi-compartment neurons might help us to understand how the neocortex optimizes cost functions. Like neocortical pyramidal neurons, neurons in our model receive sensory information and higher-order feedback in electrotonically segregated compartments. Thanks to this segregation, neurons in different layers of the network can coordinate synaptic weight updates. As a result, the network learns to categorize images better than a single layer network. Furthermore, we show that our algorithm takes advantage of multilayer architectures to identify useful higher-order representations-the hallmark of deep learning. This work demonstrates that deep learning can be achieved using segregated dendritic compartments, which may help to explain the morphology of neocortical pyramidal neurons.
Development of personality in early and middle adulthood: set like plaster or persistent change?
(2003)

Different theories make different predictions about how mean levels of personality traits change in adulthood. The biological view of the Five-factor theory proposes the plaster hypothesis: All personality traits stop changing by age 30. In contrast, contextualist perspectives propose that changes should be more varied and should persist throughout adulthood. This study compared these perspectives in a large (N = 132,515) sample of adults aged 21-60 who completed a Big Five personality measure on the Internet. Conscientiousness and Agreeableness increased throughout early and middle adulthood at varying rates; Neuroticism declined among women but did not change among men. The variety in patterns of change suggests that the Big Five traits are complex phenomena subject to a variety of developmental influences.
Nature over nurture: temperament, personality, and life span development.
(2000)

Temperaments are often regarded as biologically based psychological tendencies with intrinsic paths of development. It is argued that this definition applies to the personality traits of the five-factor model. Evidence for the endogenous nature of traits is summarized from studies of behavior genetics, parent-child relations, personality structure, animal personality, and the longitudinal stability of individual differences. New evidence for intrinsic maturation is offered from analyses of NEO Five-Factor Inventory scores for men and women age 14 and over in German, British, Spanish, Czech, and Turkish samples (N = 5,085). These data support strong conceptual links to child temperament despite modest empirical associations. The intrinsic maturation of personality is complemented by the culturally conditioned development of characteristic adaptations that express personality; interventions in human development are best addressed to these.
A power primer.
(1992)

One possible reason for the continued neglect of statistical power analysis in research in the behavioral sciences is the inaccessibility of or difficulty with the standard material. A convenient, although not comprehensive, presentation of required sample sizes is provided here. Effect-size indexes and conventional values for these are given for operationally defined small, medium, and large effects. The sample sizes necessary for .80 power to detect effects at these levels are tabled for eight standard statistical tests: (a) the difference between independent means, (b) the significance of a product-moment correlation, (c) the difference between independent rs, (d) the sign test, (e) the difference between independent proportions, (f) chi-square tests for goodness of fit and contingency tables, (g) one-way analysis of variance, and (h) the significance of a multiple or multiple partial correlation.
Gender differences in personality: a meta-analysis.
(1994)

Four meta-analyses were conducted to examine gender differences in personality in the literature (1958-1992) and in normative data for well-known personality inventories (1940-1992). Males were found to be more assertive and had slightly higher self-esteem than females. Females were higher than males in extraversion, anxiety, trust, and, especially, tender-mindedness (e.g., nurturance). There were no noteworthy sex differences in social anxiety, impulsiveness, activity, ideas (e.g., reflectiveness), locus of control, and orderliness. Gender differences in personality traits were generally constant across ages, years of data collection, educational levels, and nations.
Personality and Performance at the Beginning of the New Millennium : What Do We Know and Where Do We Go Next ?
(2001)

As we begin the new millennium, it is an appropriate time to examine what we have learned about personality-performance relationships over the past century and to embark on new directions for research. In this study we quantitatively summarize the results of 15 prior meta-analytic studies that have investigated the relationship between the Five Factor Model (FFM) personality traits and job performance. Results support the previous findings that conscientiousness is a valid predictor across performance measures in all occupations studied. Emotional stability was also found to be a generalizable predictor when overall work performance was the criterion, but its relationship to specific performance criteria and occupations was less consistent than was conscientiousness. Though the other three Big Five traits (extraversion, openness and agreeableness) did not predict overall work performance, they did predict success in specific occupations or relate to specific criteria. The studies upon which these results are based comprise most of the research that has been conducted on this topic in the past century. Consequently, we call for a moratorium on meta-analytic studies of the type reviewed in our study and recommend that researchers embark on a new research agenda designed to further our understanding of personalityperformance linkages.
A novel redis security extension for NoSQL database using authentication and encryption
(2015)

Redis is a new generation NoSQL database. Redis in its simplest form is a key value pair based data system. It supports all the data structures like variables, Linked list, arrays, strings, and queues. However unlike the conventional databases, Redis does not provide enough security for the data. Anyone can get the value if the key is known because the data is stored in the form of key value pair. Therefore such a database is unsuitable for enterprise and most practical application data. In this paper, the work is carried out to add immense security to a Redis system using following: a) Authentication Service b) Encryption Services c) Security to persistent data d) Security to blob data (multimedia data for images). The Encryption algorithm plays a very important role in the field of Database Management System. Here we make use of AES algorithm because the AES algorithm consumes least Encryption and Decryption time in comparison with RSA and DES [12]. The principle of the work is that a separate Key is created in the database whose value is an encrypted data, encrypted by symmetric key cryptography using AES. This data contains all other key values being concatenated and encrypted. Once a query is generated, first the extraction of independent data entities are made, followed by decryption of data using symmetric key. We also design a UI system to demonstrate the capabilities of the system with and without the security implementation. The result of our work shows that adding the security extension does not increase the overhead by much in terms of system resources and latency. We also extend the key-value based system to be able to store binary image data which is also stored in the encrypted pattern.
SPICE Models to Analyze Radiated and Conducted Susceptibilities of Shielded Coaxial Cables
(2010)

This paper presents simulation program with integrated circuit emphasis (SPICE) models for the analyses of the radiated and conducted susceptibilities of shielded coaxial cables. Derived from the transmission-line equations of shielded cables, these models can be directly used for the time-domain and frequency-domain analyses, and connected to nonlinear and time-varying loads with the models already available in SPICE. In the proposed models, the usual discretization of shielded cables is not needed, and both the transfer impedance and admittance have been taken into account. Results obtained with these new models are in a good agreement with those obtained by using other approaches.
Requirements Elicitation for an Inter-Organizational Business Intelligence System for Small and Medium Retail Enterprises
(2018)

Business Intelligence (BI) is on everyone's lips nowadays, since it provides businesses with the possibility to analyze their business practices and improve them. However, Small and Medium Enterprises (SME) often cannot leverage the positive effects of BI because of missing resources like personnel, knowledge, or money. Since SME pose a major form of business organization, this fact has to be overcome. As the retail industry is a substantial part of the SME branch, we propose an inter-organizational approach for a BI system for retail SME, which allows them to collaboratively collect data and perform analysis task. The aim of our ongoing research effort is the development of such a system following the Design Science Research Methodology. Within this article, the status quo of current BI practices in SME in the retail industry is analyzed through qualitative interviews with ten SME managers. Afterwards, adoption and success factors of BI systems and Inter-organizational Information Systems are worked out in a comprehensive structured literature review. Based on the status quo and the adoption and success factors, first requirements for the acceptance of an inter-organizational BI system are identified and validated in another round of qualitative interviews. This leads to nine functional requirements and three non-functional requirements, which can be used for designing and implementing an inter-organizational BI system for SME in the following research efforts.
Internet of Things-aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions
(2017)

Traditional power grids are being transformed into Smart Grids (SGs) to solve the problems of uni-directional information flow, energy wastage, growing energy demand, reliability and security. SGs offer bi-directional energy flow between service providers and consumers, involving power generation, transmission, distribution and utilization systems. SGs employ various devices for the monitoring, analysis and control of the grid, deployed at power plants, distribution centers and in consumers’ premises in a very large number. Hence, an SG requires connectivity, automation and the tracking of such devices. This is achieved with the help of Internet of Things (IoT). IoT helps SG systems to support various network functions throughout the generation, transmission, distribution and consumption of energy by incorporating IoT devices (such as sensors, actuators and smart meters), as well as by providing the connectivity, automation and tracking for such devices. In this paper, we provide the first comprehensive survey on IoTaided SG systems, which includes the existing architectures, applications and prototypes of IoT-aided SG systems. This survey also highlights the open issues, challenges and future research directions for IoT-aided SG systems.
Bert: a scheduler for best-effort and realtime paths
(1998)

We describe a new algorithm, called BERT, that can be used to schedule both best effort and realtime tasks on a multimedia workstation. BERT exploits two innovations. First, it is based on the virtual clock algorithm originally developed to schedule bandwidth on packet switches. Because this algorithm maintains a relationship between virtual time and real time, our algorithm allows us to simultaneously factor realtime deadlines into the scheduling decision, and to allocate some fraction of the processor to best effort tasks. Second, BERT includes a mechanism that allows one task to steal cycles from another. This mechanism is valuable for two reasons: it allows us to distinguish among tasks based on their importance, and it results in a robust algorithm that is not overly sensitive to the task having made a perfect reservation.